A deep learning method for recommending university patents to industrial clusters by common technological needs mining

Abstract

Industrial clusters, geographical concentrations of interconnected companies, aim to achieve technological innovation by acquiring common technology, which is the technology shared by all companies in an industrial cluster. Obtaining patents from universities is a primary way to gain common technology. However, existing patent recommendation methods have primarily focused on meeting the technological needs of individual companies, thus falling short in addressing the common technological requirements of all companies within an industrial cluster. To address the problem, we propose a deep learning (DL) method that recommends patents to industrial clusters based on common technological needs mining (DL_CTNM). The proposed method mines the common needs from patents owned by the companies and domain knowledge about potential technologies common to industries. Specifically, we mine the technological needs of the companies from their patents using long short-term memory networks and obtain their patent-based common needs by designing a candidate patent-aware attention mechanism. Then, we extract implicit technology directions from the domain knowledge using a capsule network and obtain domain knowledge-based common needs by designing an industrial cluster-aware attention mechanism. We evaluate the proposed method through offline and online experiments, comparing it to various benchmark methods. The experimental results demonstrate that our method outperforms these benchmarks in terms of recall and normalized discounted cumulative gain.